{"id":"W4311784390","doi":"10.3390/curroncol29120755","title":"Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning","year":2022,"lang":"en","type":"article","venue":"Current Oncology","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Qinghai; National Natural Science Foundation of China","keywords":"Medicine; Random forest; Machine learning; Naive Bayes classifier; Artificial intelligence; Support vector machine; Hysterectomy; Radical Hysterectomy; Cervical cancer; Internal medicine; Oncology; Algorithm; Radiology; Cancer; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009928998,0.0002214156,0.000319385,0.0006974868,0.000167934,0.0003551416,0.0001883472,0.000247214,0.0003645814],"category_scores_gemma":[0.004016026,0.0001334696,0.000345908,0.0003918811,0.0001935423,0.0002144215,0.0002567718,0.000319803,0.000102703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002465582,"about_ca_system_score_gemma":0.0003405334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680712,"about_ca_topic_score_gemma":0.002706157,"domain_scores_codex":[0.9997043,0.0001340434,0.00003666751,0.0000339023,0.00005031509,0.00004077357],"domain_scores_gemma":[0.9988776,0.0006808494,0.0001993628,0.00007533065,0.00008652374,0.00008036545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004612277,0.00004210392,0.9830734,0.00001381954,0.00003675088,0.00004963392,0.00003295515,0.001373541,0.0003603196,0.00002049976,0.0001134123,0.0144224],"study_design_scores_gemma":[0.00003303389,0.000522019,0.9670373,0.00002042867,0.0001034447,0.0002633836,0.0001639354,0.03025088,0.0009495705,0.0002602307,0.0003809842,0.00001479211],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986405,0.0003777296,0.0005752237,0.00004398794,0.000008143272,0.0000161174,0.000104628,0.000008715043,0.0002250574],"genre_scores_gemma":[0.9991342,0.0001116658,0.0004825402,0.000008496649,0.000006882089,0.000007783828,0.0001883324,0.000001222341,0.00005885796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001680712,"threshold_uncertainty_score":0.00525099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05642230595984432,"score_gpt":0.3395446202322228,"score_spread":0.2831223142723784,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}